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---
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license: mit
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base_model: microsoft/xclip-base-patch32
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: xclip-base-patch32-finetuned-custom-subset
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/huangyangyu/huggingface/runs/v8zohmjq)
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/huangyangyu/huggingface/runs/v8zohmjq)
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/huangyangyu/huggingface/runs/v8zohmjq)
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/huangyangyu/huggingface/runs/v8zohmjq)
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# xclip-base-patch32-finetuned-custom-subset
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This model is a fine-tuned version of [microsoft/xclip-base-patch32](https://huggingface.co/microsoft/xclip-base-patch32) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5862
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- Accuracy: 0.7308
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- training_steps: 1420
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|
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| 0.8431 | 0.0507 | 72 | 0.5928 | 0.7308 |
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| 0.6657 | 1.0507 | 144 | 0.7383 | 0.7308 |
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| 0.8019 | 2.0507 | 216 | 0.6047 | 0.7308 |
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| 0.6275 | 3.0507 | 288 | 0.5946 | 0.7308 |
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| 0.561 | 4.0507 | 360 | 0.6646 | 0.7308 |
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| 0.594 | 5.0507 | 432 | 0.6098 | 0.7308 |
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| 0.6472 | 6.0507 | 504 | 0.5915 | 0.7308 |
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| 0.623 | 7.0507 | 576 | 0.5948 | 0.7308 |
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| 0.5711 | 8.0507 | 648 | 0.6056 | 0.7308 |
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| 0.5967 | 9.0507 | 720 | 0.5887 | 0.7308 |
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| 0.5831 | 10.0507 | 792 | 0.5860 | 0.7308 |
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| 0.6101 | 11.0507 | 864 | 0.6044 | 0.7308 |
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| 0.6265 | 12.0507 | 936 | 0.5856 | 0.7308 |
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| 0.6373 | 13.0507 | 1008 | 0.5882 | 0.7308 |
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| 0.665 | 14.0507 | 1080 | 0.5852 | 0.7308 |
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| 0.6183 | 15.0507 | 1152 | 0.5837 | 0.7308 |
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| 0.7786 | 16.0507 | 1224 | 0.5834 | 0.7308 |
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| 0.5489 | 17.0507 | 1296 | 0.5849 | 0.7308 |
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| 0.6512 | 18.0507 | 1368 | 0.5843 | 0.7308 |
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| 0.5266 | 19.0366 | 1420 | 0.5862 | 0.7308 |
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### Framework versions
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- Transformers 4.42.0.dev0
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- Pytorch 2.1.1
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- Datasets 2.13.2
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- Tokenizers 0.19.1
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